• DocumentCode
    467731
  • Title

    Solving Multi-Objective Optimization Problems by a Bi-Objective Evolutionary Algorithm

  • Author

    Wang, Yu-Ping

  • Author_Institution
    Xidian Univ., Xi´´an
  • Volume
    2
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    1018
  • Lastpage
    1024
  • Abstract
    In this paper a novel model for multiobjective optimization problem is proposed first, in which the multiobjective optimization problem is transformed into a bi-objective optimization problem. In this bi-objective problem one objective is responsible for optimizing the quality of the solutions, and the other is to improve the distribution of the obtained nondominated solution set. Then a new crossover operator and selection scheme are designed. Based on these, a specific-designed evolutionary algorithm is presented. The simulations on five widely used benchmark problems are made and the results indicate that the proposed algorithm is efficient and outperforms the compared algorithms.
  • Keywords
    evolutionary computation; optimisation; set theory; biobjective evolutionary algorithm; crossover operator; multiobjective optimization problems; nondominated solution set; Computer science; Cybernetics; Distributed computing; Electronic mail; Evolutionary computation; Genetic programming; Machine learning; Mathematical model; Pareto optimization; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370292
  • Filename
    4370292